Embodiments of the present technique relate generally to data compression devices, and more particularly to video compression devices employing dynamic learning and control.
Digital broadcast networks that transport and deliver image sequences in near real-time, such as movies and live interviews, face a series of challenges. The first of these challenges is the digitization and transmission of enormously large digital streams of symbols over a limited bandwidth. A present day high definition television camera, for example, produces a digital stream of over two hundred million bytes per second. Data transport network costs for accommodating such high data rates are prohibitive, even if the requisite channels are available. The next challenge, therefore, faced by the transport network, is to compress these extremely high data rates into lower data rate streams for transmission over the available channels.
Further, transcontinental and intercontinental broadcast of video content, such as a sports event, requires several intermediate communication links coupled to one another for ensuring complete end-to-end delivery. This coupling is referred to as a “concatenation” and is an area of significant attention for maintaining video and audio quality and integrity. Concatenation involves multiple encode-decode processes associated with digital turn-around over satellite, wireless, and terrestrial links. Further, concatenations result in accumulation of distortions introduced during the multiple encode-decode processes, thereby reducing video quality at successive delivery feeds. Considerable efforts have been made to develop efficient data compression algorithms and standards suitable for video compression such as MPEG-2, MPEG-4, and H.264. Each of these techniques employs different parameter settings, such as a desired sample depth, a macroblock size, and a chroma format for encoding and decoding data. The compression, however, is usually lossy, and includes visible, and often, distracting artifacts in the decompressed image sequence displayed to a human user. This loss of fidelity is a result of different encoders and decoders performing rate control optimizations independent of the other elements of the concatenation. An MPEG-2 encoder, for example, employs a relatively larger macroblock for encoding, and therefore, may introduce artifacts in an image sequence traversing a concatenated chain of compressive devices. As a result, an H.264 encoder positioned further down the concatenated chain and employing a much smaller macroblock, incurs wasted computation and bandwidth to reproduce, with high fidelity, the artifacts and errors introduced by the MPEG-2 encoder.
Further, as the number of encoders and decoders in the concatenation grows, the large number of video compression parameters and the types of scenes to be analyzed result in a combinatorial explosion in the growth of the search space for determining optimal encoder and decoder parameter settings. A technique that improves this search in near real-time and optimizes performance along an entire chain of encoding and decoding devices would be of significant benefit in terms of reducing the cost of video transport while maintaining video quality.
It may therefore be desirable to develop an adaptive data compression system optimized for repeated encoding and decoding of different types of data sequences along a concatenated chain of compressive devices. Additionally, there is a need for a system configured to allow a large number of channels to be carried within a limited bandwidth with acceptable video quality.